Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction

The accurate prediction of the solar diffuse fraction (DF), sometimes called the diffuse ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse irradiance research is discussed and then three robust, machine learning (ML) models are examined using a large dataset (almost eight years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid adaptive network-based fuzzy inference system (ANFIS), a single multi-layer perceptron (MLP) and a hybrid multi-layer perceptron grey wolf optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various solar and DF irradiance data, from Spain. The results were then evaluated using frequently used evaluation criteria, the mean absolute error (MAE), mean error (ME) and the root mean square error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance in both the training and the testing procedures.     

多集值信息表中的多粒度模糊决策论粗糙集

多粒度粗糙集和决策论粗糙集是Pawlak粗糙集的重要推广,目前已成为人工智能研究的热点.然而,它们大多处理的都是单值信息系统中的问题.而实际生活中绝大多数都是处理多值问题,为了解决这一问题,在多集值信息表中将多粒粗糙集与模糊决策论粗糙集相结合进行研究,提出了其在乐观,悲观情形下的上下近似,研究了一些相关性质并给出了多集值信息表中的多粒度模糊决策论粗糙集精度、粗度的概念,最后通过一个具体例子验证其有效性.     

基于VIKOR和关联系数的犹豫三角模糊语言多属性决策方法

针对属性评价值为犹豫三角模糊语言集的多属性决策问题,提出一种基于VIKOR方法的犹豫三角模糊语言多属性决策方法.首先定义了犹豫三角模糊语言集的相关概念.然后运用VIKOR和关联系数方法,在可接受优势和决策过程稳定的条件下对方案进行择优,在理论分析的基础上,提出了这种新方法的计算步骤.并构建了确定最优属性权重的非线性规划模型,研究了当专家权重和属性权重未知情况下的犹豫三角模糊语言多属性决策方法.最后通过实例说明了该方法的有效性和可行性.     

An Improved Composite Multiscale Fuzzy Entropy for Feature Extraction of MI-EEG

Motor Imagery Electroencephalography (MI-EEG) has shown good prospects in neurorehabilitation, and the entropy-based nonlinear dynamic methods have been successfully applied to feature extraction of MI-EEG. Especially based on Multiscale Fuzzy Entropy (MFE), the fuzzy entropies of the τ coarse-grained sequences in τ scale are calculated and averaged to develop the Composite MFE (CMFE) with more feature information. However, the coarse-grained process fails to match the nonstationary characteristic of MI-EEG by a mean filtering algorithm. In this paper, CMFE is improved by assigning the different weight factors to the different sample points in the coarse-grained process, i.e., using the weighted mean filters instead of the original mean filters, which is conductive to signal filtering and feature extraction, and the resulting personalized Weighted CMFE (WCMFE) is more suitable to represent the nonstationary MI-EEG for different subjects. All the WCMFEs of multi-channel MI-EEG are fused in serial to construct the feature vector, which is evaluated by a back-propagation neural network. Based on a public dataset, extensive experiments are conducted, yielding a relatively higher classification accuracy by WCMFE, and the statistical significance is examined by two-sample t-test. The results suggest that WCMFE is superior to the other entropy-based and traditional feature extraction methods.     

基于改进VIKOR方法的地震应急物资运输决策问题研究

地震灾害应急救援物资方案的合理选择对与减少人员伤亡,降低灾民的财产损失具有重要影响。本文针对属性权重未知情形下的地震应急物资运输方案决策问题,提出了一种Pythagorean模糊不确定语言与前景理论相结合的改进VIKOR决策方法,即PFUL-PT-VIKOR法。首先,采用Pythagorean模糊不确定语言用于描述和融合专家对地震应急物资运输方案在考虑多种属性影响下的感知信息;其次,利用主客观融合法对属性权重进行求解;然后,提出基于前景理论的改进VIKOR法并得出方案排序;最后,通过算例分析,对所提出方法的有效性和实践性给予验证。结果表明,PFUL-PT-VIKOR模型有助于增强决策专家对不确定突发情景信息感知的知识表示能力,解决属性赋权过于主观或过分依赖样本的困难,并突围了应急决策者隐性心理行为较难定量应用的思维定势,增强了模型的现实适用性,为地震应急物资是否合理运输提供决策支持。     

模糊选择函数半序合理的充分条件

在G?del t-模下,研究了模糊选择函数的半序合理性.首先给出了模糊选择函数的合理性条件FA1.然后研究了该条件与模糊选择函数半序合理性之间的关系,得到了半序合理的一个充分条件.     

预不变凸模糊集的一些性质

在下半连续的条件下,给出了一个模糊集是预不变凸模糊集的充分条件,并将模糊凸集上相关性质在模糊不变凸集上作了相应的推广.所给出的条件和证明都是新的.